Input File Specification for Face Liveness Detection

Image Specification

  • The input image should be a frontal selfie.
frontal selfie
  • All atributes on the image is calculated on the passive liveness check. Please input the original image without any cropping and without any enhancement on the camera filter or photoshopped, because we need more context on passive liveness evaluation.
  • The liveness detection works best in such environments:
    • On a uniform background
    • Good image quality such as: not dark, sharp or not blurred, has sufficient contrast, not overshadowed, and no spotlight present on the face.
    • No wearables occluded the face

Passive Liveness Requirement Check

Image Input Specification

The submitted image input should fulfill the minimum requirements below:
Image Setting
Minimum camera pixel
Above 2 MP
Image file size
Minimum size 100 KB and the maximum are 2 MB
Image compression recommendation
Bicubic, with minimum JPEG quality 80%

Face Appearance Check: Face Detection and Pose Check

The submitted Face input should fulfill the minimum requirements below:
Image Setting
Minimum face area size
300 x 300 pixels, or adjust on 600 x 600 pixels
Ideal face posture
Frontal face
Face orientation position
Total face in the frame
1 face
Before processing the image, we will ensure there is a face object on the frame using face detection. If the image input has multiple faces, the algorithm will choose the main bigger face and will check the face pose using head poses analysis to make sure that a face object is in frontal face position.
The algorithm will detect the main bigger face on the frame
The algorithm will detect the main bigger face on the frame
The head pose estimation is needed on this part to prevent over tilted pose that might be reduced prediction accuracy for face occlusion and liveness check. On the other side, the head pose check might help to reject some spoof from paper or replay video.

Face Appearance Check: Face Occlusion Check

face occlusion is an algorithm to check whether the face is occluded by an object or any wearables. The facial areas that is checked by the occlusion are:
  • Forehead
  • Left and right eyebrow
  • Left and right eyes
  • Left and right cheeks
  • Nose
  • Mouth

Image Quality Assessment

The image quality assessment will check the quality of the face area. Attributes that are checked by the algorithm consist of:
  • Sharpness: an image quality attribute to evaluate whether the face area is blurred or not. The value below 0.1 indicates a very blur image, the value between 0.1 to 0.2 indicates doubt are, and the best condition of the sharpness is above 0.2.
  • Contrast: an image quality attribute to evaluate the face area has enough contrast conditions. The value below 0.5 indicates low contrast, the value between 0.5 to 0,6 indicates a doubt condition, and enough contrast should be above 0.6.
  • Brightness: an image quality attribute to evaluate whether the face area has enough lightning condition. The value below 0.3 indicates a very dark condition, the value between 0.3 to 0.4 indicates a doubtful condition, and the best brightness condition is above 0.4.
Face Quality Attribute
s < 0.1
0.1 ≤ s < 0.2
s ≥ 0.2
s < 0.5
0.5 ≤ s < 0.6
s ≥ 0.6
s < 0.3
0.3 ≤ s < 0.4
s ≥ 0.4

Spoof Component Check

For this liveness detection, the algorithm logic will return 3 recommendations for accepting or rejecting a selfie. The spoof component will check the specific defined obvious characteristics of a suspect spoof selfie by defining visual characteristics without giving selfie source information.
The defined obvious spoof artifacts consist of Nodeflux passive liveness detection has been trained is mentioned below:
  • Spoof Edge: it’s mostly found on paper and replay attacks which has border characteristic
  • Deformation: it’s mostly found on a 3D paper mask, cut mask, and 2D paper mask
  • Artifact: It's mostly found on replay attacks, which gives characteristic moire and reflection. The Moire pattern is a character of obvious pixelated-like pattern that will be found on replay attack from a device screen, LCD, or monitor. Reflection is sometimes found on a replay attack case from a screen photo medium that reflects an image from the device.
Spoof Characteristic
Border Edge
Obvious Print Attack on Printed paper with border
Border Edge
Border from device
Un-natural deformation
Printed Mask paper with fine cut
Cut Border
Printed Mask paper with border
Obvious on laptop screen attack contain light reflection and shadow reflection
un-Obvious on laptop screen attack contain light reflection